1. Field of the Invention
The present invention generally relates to the field of labor or workforce management, and more specifically to a computerized method for determining the distribution of traffic and providing labor scheduling recommendations based on foot traffic information for facilities such as retail stores, malls, casinos, or the like.
2. Related Prior Art
Traditionally, labor staffing was performed manually by the management of businesses. The invention of computer technology facilitated the labor staffing process by allowing humans to use computer programs. More recently, computer methods have been developed to determine improved workforce schedules. Examples include Gary M. Thompson (A Simulated-Annealing Heuristic For Shift Scheduling Using Non-continuously Available Employees, Computer Ops. Res. Vol. 23, No. 3, pp 275-288, 1996) and U.S. Pat. No. 6,823,315.
Gary M. Thompson described a method of labor scheduling using a simulated annealing process, which heuristically compares a trial schedule from an incumbent schedule. U.S. Pat. No. 6,823,315 is directed to a cost-effective workforce scheduling system, which takes into consideration workforce requirements including employee preferences and job skills in addition to using a simulated annealing function.
An essential problem for labor scheduling is to accurately predict staffing needs for stores. Stores tend to have varied foot traffic during different seasons. For example, the period between Thanksgiving and Christmas is usually very busy and thus more traffic is expected. On the other hand, a Tuesday afternoon in a month with no national holiday may expect less traffic than normally observed. Therefore, foot traffic for a given store is an important factor for predicting store sales and staffing needs for that given store. Previous scheduling approaches have not come to realize the importance of store traffic and often used other data, such as historical store sales, as the main factor for predicting future store sales and labor demands. However, historical store sales information may not be a good indication of potential sales, because being short handed at busy seasons is likely to have a negative impact on sales. Using old sales data to predict future sales is likely to suffer from repetitive mistakes.
Meanwhile, store traffic is a better representation of staffing demands and is perhaps the most accurate leading indicator for future sales. Research shows that, for example, a steady decline in store traffic indicates that sales will similarly decline within approximately 13 months. Therefore, if a store only sees that sales are steady but is unaware that the store traffic has declined, that store won't be prepared to take corrective action before facing a future loss in sales. Each shopper that walks through the door represents a sales opportunity. Syncing store labor to foot traffic and conversion rate does not require the retailers to spend more; rather it will allow more efficient management of labor. No prior invention has developed a labor scheduling method using traffic data as the leading input for predicting labor demands and recommendations.
Thus, it is a primary objective of this invention to provide a computerized labor scheduling method using traffic information.